This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relativ...This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.展开更多
Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due t...Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due to extreme climatic conditions and facilitate the cultivation of subsequent crops on the same land,thereby enhancing overall agricultural efficiency.In this review,we synthesize current information on flowering time regulation in rapeseed through an integrated analysis of its genetic,hormonal,and environmental dimensions,emphasizing their crosstalk and implications for yield.We consolidate multi-omics evidence from population genetics,functional genomics,and systems biology to create a haplotype-based framework that overcomes the trade-off between flowering time and yield,providing support for the precision breeding of early-maturing cultivars.The insights presented here could inform future research on flowering time regulation and guide strategies for increasing rapeseed productivity.展开更多
This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality...This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.展开更多
Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based c...Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based chain rescue is crucial in managing severe trauma.A patient with chest and abdominal injuries and hemorrhagic shock was transferred from an ambulance to our hospital.Our trauma team-initiated pre-hospital first aid,utilized an emergency green channel,and conducted rapid ultrasound,collaborating across disciplines.The patient eventually recovered and was discharged.展开更多
Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remain...Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.展开更多
Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while...Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while the potential for further vibration reduction remains debated,largely due to unclear underlying mechanisms.In light of the popularization of electronic detonators and the representativeness of double-hole configurationsfor multiple blastholes,it is essential to investigate the vibration characteristics induced by time-delayed double blastholes.Therefore,a series of doubleborehole experimental blasts was conducted in an underground roadway to clarify the variation in vibration from single-hole to dual-hole conditions.Based on the experimental data and inherent limitations,an exact full-fieldtheoretical model was further employed to systematically analyze the effects of delay time,charge length,and borehole inclination angle on vibrations induced by various doublehole configurations.The experimental data and theoretical analysis reveal that the general scaled distance effectively predicts vibrations in delayed blasting but does not reflectvibration reduction.Increasing delay time causes fluctuatingPPVs,which stabilize slightly above single-hole PPVs as delay times exceed a certain value.The delayed blasting primarily reduces near-fieldfrequencies.Longer charge lengths in double boreholes increase PPV levels and attenuation rates within a certain length,and the vibration behavior of combined long and short charge lengths is governed by the long blasthole.Larger blasthole inclination angles enhance vibration amplitude and reduce PPV attenuation rates.Optimizing inclination angles is more critical than adjusting delay times,and parallel boreholes offer the best vibration control.展开更多
Blue calico,which dates back more than 800 years,is a traditional blue-and-white-printed fabric in Nantong,a city in East China's Jiangsu Province.Blue calico is renowned for its simple,yet elegant,patterns,the st...Blue calico,which dates back more than 800 years,is a traditional blue-and-white-printed fabric in Nantong,a city in East China's Jiangsu Province.Blue calico is renowned for its simple,yet elegant,patterns,the striking beauty of its blue-and-white harmony and,especially,for its printing and dyeing being performed by hand.展开更多
Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban m...Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban memories.During the past decade,she has focused on creating freeform sculptures,and she has launched a global,cross-disciplinary-artist-collaboration project,which has involved the preservation of archives for contemporary artistic creation.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
1.Introduction.Since the Industrial Revolution,the partial pressure of atmospheric carbon dioxide(pCO_(2))has increased markedly,rising from approximately 280 ppm(1 ppm=1μL/L)to about 420 ppm.This escalation has inte...1.Introduction.Since the Industrial Revolution,the partial pressure of atmospheric carbon dioxide(pCO_(2))has increased markedly,rising from approximately 280 ppm(1 ppm=1μL/L)to about 420 ppm.This escalation has intensified global warming,with 2024 the hottest year on record since 1850.The global mean temperature now stands 1.46℃ above the pre-industrial average(1850-1900),a value already approaching the 1.5℃ threshold set by the Paris Agreement(NOAA,2025).展开更多
文摘This study investigates prescribed-time position tracking control for electromagnetic satellite formations subject to model uncertainties and external disturbances.Using the Clohessy-Wiltshire equations as the relative motion dynamics model,a prescribed time output feedback control strategy is proposed.A prescribed-time extended state observer is designed to estimate the relative velocity and external disturbances.The disturbance estimates are then used as the feedforward component of the controller.Building on this framework,a novel prescribed-time active disturbance rejection control strategy for position tracking is developed via a backstepping control design.The convergence of the extended state observer and the stability of the closed-loop system are rigorously analyzed using Lyapunov stability theory.Numerical simulations are performed to validate the effectiveness of the proposed controller.
基金supported by the National Key Research and Development Program of China(2022YFD1200400)the National Natural Science Foundation of China(32272111)+4 种基金Special fund for youth team of the Southwest Universities(SWU-XJPY202306)Chongqing Natural Science Foundation(CSTB2024NSCQLZX0012)Modern Agro-industry Technology Research System(CARS-12)Chongqing Modern Agricultural Industry Technology System(COMAITS202504)Biological Breeding-National Science and Technology Major Project(2022ZD04008).We sincerely appreciate the Plant Editors team for English language editing of the manuscript,which significantly improved its clarity and overall quality.
文摘Flowering time is a critical agronomic trait with a profound effect on the productivity and adaptabillity of rapeseed(Brassica napus L.).Strategically advancing flowering time can reduce the risk of yield losses due to extreme climatic conditions and facilitate the cultivation of subsequent crops on the same land,thereby enhancing overall agricultural efficiency.In this review,we synthesize current information on flowering time regulation in rapeseed through an integrated analysis of its genetic,hormonal,and environmental dimensions,emphasizing their crosstalk and implications for yield.We consolidate multi-omics evidence from population genetics,functional genomics,and systems biology to create a haplotype-based framework that overcomes the trade-off between flowering time and yield,providing support for the precision breeding of early-maturing cultivars.The insights presented here could inform future research on flowering time regulation and guide strategies for increasing rapeseed productivity.
基金National Natural Science Foundation of China under Grant Nos.51979205 and 51939008。
文摘This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.
基金Jiangsu Provincial Hospital Association Hospital Management Innovation Research Fund(Project Ni.:JSYGY-3-2025-267)。
文摘Severe trauma often involves complex injuries,leading to high disability and fatality rates.Effective treatment requires prompt and coordinated efforts across multiple disciplines to enhance success rates.Time-based chain rescue is crucial in managing severe trauma.A patient with chest and abdominal injuries and hemorrhagic shock was transferred from an ambulance to our hospital.Our trauma team-initiated pre-hospital first aid,utilized an emergency green channel,and conducted rapid ultrasound,collaborating across disciplines.The patient eventually recovered and was discharged.
基金This study is financed by the European Union-NextGenerationEU,through the National Recovery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001.
文摘Covert timing channels(CTC)exploit network resources to establish hidden communication pathways,posing signi cant risks to data security and policy compliance.erefore,detecting such hidden and dangerous threats remains one of the security challenges. is paper proposes LinguTimeX,a new framework that combines natural language processing with arti cial intelligence,along with explainable Arti cial Intelligence(AI)not only to detect CTC but also to provide insights into the decision process.LinguTimeX performs multidimensional feature extraction by fusing linguistic attributes with temporal network patterns to identify covert channels precisely.LinguTimeX demonstrates strong e ectiveness in detecting CTC across multiple languages;namely English,Arabic,and Chinese.Speci cally,the LSTM and RNN models achieved F1 scores of 90%on the English dataset,89%on the Arabic dataset,and 88%on the Chinese dataset,showcasing their superior performance and ability to generalize across multiple languages. is highlights their robustness in detecting CTCs within security systems,regardless of the language or cultural context of the data.In contrast,the DeepForest model produced F1-scores ranging from 86%to 87%across the same datasets,further con rming its e ectiveness in CTC detection.Although other algorithms also showed reasonable accuracy,the LSTM and RNN models consistently outperformed them in multilingual settings,suggesting that deep learning models might be better suited for this particular problem.
基金supported by the National Natural Science Foundation of China(Grant Nos.42407267 and 52374152)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20220975).
文摘Time-delayed blasting is widely utilized in engineering to mitigate induced vibration hazards and enhance fragmentation.The underlying vibration reduction principle is the decrease of the charge weight per delay,while the potential for further vibration reduction remains debated,largely due to unclear underlying mechanisms.In light of the popularization of electronic detonators and the representativeness of double-hole configurationsfor multiple blastholes,it is essential to investigate the vibration characteristics induced by time-delayed double blastholes.Therefore,a series of doubleborehole experimental blasts was conducted in an underground roadway to clarify the variation in vibration from single-hole to dual-hole conditions.Based on the experimental data and inherent limitations,an exact full-fieldtheoretical model was further employed to systematically analyze the effects of delay time,charge length,and borehole inclination angle on vibrations induced by various doublehole configurations.The experimental data and theoretical analysis reveal that the general scaled distance effectively predicts vibrations in delayed blasting but does not reflectvibration reduction.Increasing delay time causes fluctuatingPPVs,which stabilize slightly above single-hole PPVs as delay times exceed a certain value.The delayed blasting primarily reduces near-fieldfrequencies.Longer charge lengths in double boreholes increase PPV levels and attenuation rates within a certain length,and the vibration behavior of combined long and short charge lengths is governed by the long blasthole.Larger blasthole inclination angles enhance vibration amplitude and reduce PPV attenuation rates.Optimizing inclination angles is more critical than adjusting delay times,and parallel boreholes offer the best vibration control.
文摘Blue calico,which dates back more than 800 years,is a traditional blue-and-white-printed fabric in Nantong,a city in East China's Jiangsu Province.Blue calico is renowned for its simple,yet elegant,patterns,the striking beauty of its blue-and-white harmony and,especially,for its printing and dyeing being performed by hand.
文摘Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban memories.During the past decade,she has focused on creating freeform sculptures,and she has launched a global,cross-disciplinary-artist-collaboration project,which has involved the preservation of archives for contemporary artistic creation.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the National Natural Science Foundation of China(Grant Nos.42425305,42293290,and 42172216).
文摘1.Introduction.Since the Industrial Revolution,the partial pressure of atmospheric carbon dioxide(pCO_(2))has increased markedly,rising from approximately 280 ppm(1 ppm=1μL/L)to about 420 ppm.This escalation has intensified global warming,with 2024 the hottest year on record since 1850.The global mean temperature now stands 1.46℃ above the pre-industrial average(1850-1900),a value already approaching the 1.5℃ threshold set by the Paris Agreement(NOAA,2025).
